Nsubuga Mike, Kintu Timothy Mwanje, Please Helen, Stewart Kelsey, Navarro Sergio M
The Infectious Diseases Institute, Makerere University, P. O. Box 22418, Kampala, Uganda.
Faculty of Health Sciences, University of Bristol, Bristol, BS40 5DU, UK.
BMC Emerg Med. 2025 Jan 23;25(1):14. doi: 10.1186/s12873-025-01175-2.
Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these settings, though its predictive accuracy remains under debate. This study evaluates the effectiveness of machine learning (ML) models in predicting triage decisions and compares their performance to the KTS.
Data from 4,109 trauma patients at Soroti Regional Referral Hospital, a rural hospital in Uganda, were used to train and evaluate four ML models: Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The models were assessed in regard to accuracy, precision, recall, F1-score, and AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic curve). Additionally, a multinomial logistic regression model using the KTS was developed as a benchmark for the ML models.
All four ML models outperformed the KTS model, with the RF and GB both achieving AUC-ROC values of 0.91, compared to 0.62 (95% CI: 0.61-0.63) for the KTS (p < 0.01). The RF model demonstrated the highest accuracy at 0.69 (95% CI: 0.68-0.70), while the KTS-based model showed an accuracy of 0.54 (95% CI: 0.52-0.55). Sex, hours to hospital, and age were identified as the most significant predictors in both ML models.
ML models demonstrated superior predictive capabilities over the KTS in predicting triage decisions, even when utilising a limited set of injury information about the patients. These findings suggest a promising opportunity to advance trauma care in LMICs by integrating ML into triage decision-making. By leveraging basic demographic and clinical data, these models could provide a foundation for improved resource allocation and patient outcomes, addressing the unique challenges of resource-limited settings. However, further validation is essential to ensure their reliability and integration into clinical practice.
创伤性损伤是全球发病和死亡的主要原因，对低收入和中等收入国家（LMICs）的人群影响尤为严重。坎帕拉创伤评分（KTS）在这些环境中经常用于分诊，但其预测准确性仍存在争议。本研究评估机器学习（ML）模型在预测分诊决策方面的有效性，并将其性能与KTS进行比较。
乌干达一家农村医院索罗蒂地区转诊医院4109名创伤患者的数据用于训练和评估四种ML模型：逻辑回归（LR）、随机森林（RF）、梯度提升（GB）和支持向量机（SVM）。从准确性、精确性、召回率、F1分数和AUC-ROC（受试者工作特征曲线下面积）方面对模型进行评估。此外，开发了一个使用KTS的多项逻辑回归模型作为ML模型的基准。
所有四个ML模型的表现均优于KTS模型，RF和GB的AUC-ROC值均达到0.91，而KTS的AUC-ROC值为0.62（95%CI：0.61-0.63）（p<0.01）。RF模型的准确率最高，为0.69（95%CI：0.68-0.70），而基于KTS的模型准确率为0.54（95%CI：0.52-0.55）。性别、到达医院的时间和年龄被确定为两个ML模型中最显著的预测因素。
即使在使用关于患者的有限损伤信息时，ML模型在预测分诊决策方面也表现出优于KTS的预测能力。这些发现表明，通过将ML纳入分诊决策，在LMICs推进创伤护理方面存在一个有前景的机会。通过利用基本的人口统计学和临床数据，这些模型可以为改善资源分配和患者预后提供基础，应对资源有限环境中的独特挑战。然而，进一步验证对于确保其可靠性并整合到临床实践中至关重要。
